Driver Distraction Detection with a Camera Vision System

Kutila, Matti; Jokela, Maria; Markkula, Gustav; Rue, M.R.

doi:10.1109/icip.2007.4379556

Cited by 86 publications

(69 citation statements)

References 5 publications

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“…The sensitivity of the model based on generic training set is .88, and the specificity of the model is .94. Our accuracy in detecting texting while driving is comparable to or sometimes higher than other attempts to detect cognitive distraction and visual distraction (Kutila, Jokela, Markkula, & Rue, 2007). For example, achieved an average accuracy of 81.1% in detecting driver cognitive distraction using Support Vector Machines.…”

Section: Prediction Based On Generic Training Setmentioning

confidence: 57%

Mutual interferences of driving and texting performance

Chaparro

et al. 2015

Computers in Human Behavior

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Section: Prediction Based On Generic Training Setmentioning

confidence: 57%

Mutual interferences of driving and texting performance

Chaparro

et al. 2015

Computers in Human Behavior

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“…5). Because the rUFOV concept is based on gaze and head orientations as input calculations have been added to the TrackEye software [9]. The experiments indicated that performance of the low-cost eye tracking system is sufficient for evaluating whether the proposed rUFOV algorithm works, but when implementing real in-vehicle product, a more sophisticated eye tracker is needed.…”

Section: Experimental Equipmentmentioning

confidence: 99%

Measurement of Driver’s Visual Attention Capabilities Using Real-Time UFOV Method

Danno¹,

Kutila

Kortelainen

2011

Int. J. ITS Res.

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This paper proposes a new real-time method to measure the driver's useful field of view (UFOV) while driving a car in ordinary traffic situations in an urban environment. This is called the real-time useful field of view (rUFOV) method to discriminate it from conventional UFOV measurement, which is typically performed offline and with laboratory equipment developed by Visual Awareness Inc. The proposed real-time method first tracks traffic objects that appear in the driver's peripheral vision using a road video camera, checks the degree of the driver's attention to these objects using a driver monitoring camera, and finally calculates the percentage reduction in the driver's UFOV using a database acquired over an extended period of time. Preliminary results showed better performance than originally expected. The rUFOV method was then incorporated into a driving simulation environment to enable more precise measurement of the driver's gaze angle. This enabled the performance of safer tests for identifying conditions under which mental load reduced the driver's visual capabilities, thus increasing the possibility of hasty driving, as well as the incorporation of more accurate control parameters into simulation software for risky driving scenarios. Consequently, this paper proposes a new methodology for measuring the driver's UFOVas a potential real-time driver support system with automatic intrusive HMI adaptation and immediate alarm functions. The evaluation was conducted in two phases. First, the system was tested in real traffic using typical vehicle equipment and technically worked with a performance level of 81%.In the second phase, more test runs were performed in the simulator environment, which enabled near accident scenarios to be created without risking traffic safety and it was also measured its reaction time..

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“…Kutila et al [8] describes a facility for monitoring the distraction of a driver and presents some early evaluation results. They present a module that is able to detect the driver's visual and cognitive workload by fusing stereo vision and lane tracking data, running both rule-based and support-vector machine (SVM) classification methods.…”

Section: Related Workmentioning

confidence: 99%

Low-Cost Vehicle Driver Assistance System for Fatigue and Distraction Detection

Sendra

García

Jiménez

et al. 2017

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. In recent years, the automotive industry is equipping vehicles with sophisticated, and often, expensive systems for driving assistance. However, this vehicular technology is more focused on facilitating the driving and not in monitoring the driver. This paper presents a low-cost vehicle driver assistance system for monitoring the drivers activity that intends to prevent an accident. The system consists of 4 sensors that monitor physical parameters and driver position. From these values, the system generates a series of acoustic signals to alert the vehicle driver and avoiding an accident. Finally the system is tested to verify its proper operation.

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Driver Distraction Detection with a Camera Vision System

Cited by 86 publications

References 5 publications

Mutual interferences of driving and texting performance

Mutual interferences of driving and texting performance

Measurement of Driver’s Visual Attention Capabilities Using Real-Time UFOV Method

Low-Cost Vehicle Driver Assistance System for Fatigue and Distraction Detection

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